Data Preparation

Model your data as a series of process observations or measures that are associated with an outcome of interest.  Compose each observation as a common set of features (aka., independent variables or factors) along with the associated outcome.  Both features and outcomes are either numerical (dates, times, ages, etc.), binary (yes/no, true/false, etc.) or categorical (Gender, Service line , Floor unit, Shift, DRG, etc.).

 

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Submission & Processing

Transfer data via a secure dedicated sFTP account

  1. Our automation process will email you when data is received by our sFTP server in a HIPAA compliant HITRUST environment.
  2. Your data is automatically routed into a preprocessing stage which notifies you of errors which would stop further processing.
  3. Near real-time processing is an option

Processing

  1. We engineer features, as needed, to remove noise and improve the quality of findings and predictive models.
  2. Our machine learning engine (MLE) applies unsupervised learning algoithms to bin the data and remove noise as needed.
  3. The MLE associates patterns in the data with outcomes of interest (data mining)
  4. The MLE tests multiple supervised learning algorithms, including deep learning, to choose the best predictive model for the business goal.
  5. We use both multi-fold cross validation and separate sample testing to measure and verify the predictivity and accuracy in the data.

 Automated Decision Rule Generation Provides Quick Insight

  1. Automatic generation of simple predictive decisioning rules allows you to easily distinguish factors associated with outcomes of interest, extract actionable factors and design corrective process interventions
  2. We periodically retrain and test the predictive models to provide ongoing predictions based on fresh learning

 

News

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